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Law Feeds

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses it probes each entity with ai_visibility_check, returns ranked list, treats first entity as subject, and requires _apiKey for Anthropic. Annotations already indicate readOnly, idempotent, non-destructive; description adds meaningful context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences cover purpose, process, and use case. Front-loaded with action, efficient with no redundant words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 4 parameters, no output schema, and annotations present, the description fully covers what the tool does, how it works, parameter roles, and return type. States entity limit (2-8). Complete for a scanning/comparison tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds significant meaning: first entity is subject, models have defaults, _apiKey conditional on model choice, context disambiguates. These details are not in the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it compares AI visibility across multiple entities, ranks by score, and surfaces most/least recognized. Distinguishes from sibling ai_visibility_check (single entity) and compare_entities (likely different comparison).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly mentions competitive AI-marketing audits and provides an example question. Implicitly distinguishes from single-probe ai_visibility_check. Could be more explicit about when not to use or alternatives like compare_entities.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tool families overlap heavily: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded all route through the same 5,721 tools, polymarket_edges/polymarket_arbitrage/bet_research/polymarket_fill_risk all target prediction-market opportunities, and ai_visibility_check vs scan_competitor_ai_presence cover the same probe. An agent would struggle to pick the right one without reading every description carefully.

Naming Consistency3/5

There are coherent subfamilies (ask_pipeworx_*, polymarket_*, remember/recall/forget, list/read/fetch_feed), but the overall set mixes verb-first names (list_feeds, validate_claim, fetch_feed) with noun-first names (entity_profile, bet_research, deep_research) and adjective-led names (recent_alerts, recent_changes). The inconsistency is noticeable but not chaotic.

Tool Count2/5

34 tools is heavy for a server branded 'Law Feeds,' and many tools are off-domain (Polymarket betting, npm dependency scanning, AI visibility marketing, generic memory). The breadth could justify a larger catalog, but the overlapping research/Polymarket tools inflate the count beyond what the surface needs.

Completeness3/5

As a general data-gateway, the set is fairly complete: routing, grounded answers, deep research, entity resolution, comparison, subscriptions, memory, and feedback are all covered. Relative to the 'Law Feeds' identity, though, the surface is shallow — only list_feeds, read_feed, and fetch_feed serve that purpose, with no feed search, management, or update capabilities.

Resources